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					Randomized Radon Transforms for
   Biometric Authentication via
      Fingerprint Hashing

            Mariusz H. Jakubowski
           Ramarathnam Venkatesan
             Microsoft Research


 2007 ACM Digital Rights Management Workshop
            Alexandria, VA (USA)
               October 29, 2007
                                              Introduction
   • Biometrics: “What you are”
          – Measurements over bodily features (e.g., fingerprints)
          – Applications for security and convenience

   • Biometric hashing
          – One-way extraction of information from biometric data
          – Human identifiers for DRM authentication

   • Goals of our work:
          – New method for fingerprint hashing
          – Applications to strengthen and streamline DRM
            security

2007 ACM Digital Rights Management Workshop                  October 29, 2007   2
                                              Overview
         Fingerprint hashing via Radon transform

   •    Introduction
   •    Fingerprint hashing
   •    Experimental results
   •    Conclusion



2007 ACM Digital Rights Management Workshop              October 29, 2007   3
                            Fingerprint Hashing
          Conversion of fingerprints to one-way hashes
                for authentication applications

   •      Fingerprint hash: An irreversible compressed
          representation of fingerprint data, extracted according
          to a secret key.

   •      Basic procedure:
         –      Compute various metrics over a fingerprint image and
                combine these into a hash vector.
         –      Apply error correction and other methods to increase
                hash robustness.

2007 ACM Digital Rights Management Workshop            October 29, 2007   4
                                Radon Transform
   • Standard: (x,y)  (θ, ρ), where θ and ρ denote angles and
     distances of lines.
   • Line at angle θ and distance ρ from origin will result in high value of
     transform coefficient (θ, ρ).




               Original image                                R(θ, ρ)

    Hash transform: This line-based metric is replaced by a custom metric.
2007 ACM Digital Rights Management Workshop              October 29, 2007      5
            Randomizing the Transform
   • Standard:
          – Exhaustively enumerate all lines.
          – Typical metric: Compute projections of lines onto
            image.
   • Randomized:
          – Generate a pseudorandom sequence of lines, using a
            secret hashing key.
          – Simpler metric: Compute crossing counts of lines with
            image (i.e., number of times each line crosses or
            grazes fingerprint curves).

   • Randomized transform leads to hashing scheme.
2007 ACM Digital Rights Management Workshop       October 29, 2007   6
        Fingerprint Hashing: Example




Scanned fingerprint




Metric: Crossing count with
random lines and curves

2007 ACM Digital Rights Management Workshop   October 29, 2007   7
        Fingerprint Hashing: Example




Scanned fingerprint           Cleaned fingerprint
                                 o Generic clean-up: Filters, thresholds, etc.
                                 o Specialized methods: VeriFinger (Neurotechnologija, Inc.)



Metric: Crossing count with
random lines and curves

2007 ACM Digital Rights Management Workshop                                                October 29, 2007   8
        Fingerprint Hashing: Example


                                                    5 random lines




Scanned fingerprint           Cleaned fingerprint




Metric: Crossing count with
random lines and curves

2007 ACM Digital Rights Management Workshop                          October 29, 2007   9
        Fingerprint Hashing: Example
                                                                                 25 21 24 25 25



                                                    5 random lines




Scanned fingerprint           Cleaned fingerprint




Metric: Crossing count with
random lines and curves

2007 ACM Digital Rights Management Workshop                          October 29, 2007         10
        Fingerprint Hashing: Example
                                                                                  25 21 24 25 25



                                                    5 random lines



                                                                                 22 17 21 23 23
                                                                                 22 22 27 24 25
                                                                                 14 23 25 27 25


Scanned fingerprint           Cleaned fingerprint   15 random lines




Metric: Crossing count with
random lines and curves

2007 ACM Digital Rights Management Workshop                           October 29, 2007            11
        Fingerprint Hashing: Example
                                                                                   25 21 24 25 25



                                                     5 random lines



                                                                                  22 17 21 23 23
                                                                                  22 22 27 24 25
                                                                                  14 23 25 27 25


Scanned fingerprint           Cleaned fingerprint   15 random lines




                                                                                   3 24 44 27 32
                                                                                   8 16 24 37 31
Metric: Crossing count with
random lines and curves
                                                                            Hashes (crossing counts)
                                                    10 random curves
2007 ACM Digital Rights Management Workshop                            October 29, 2007            12
                Some Metrics for Hashing
   • Counts of crossings with lines and curves
   • Curvatures of fingerprint lines within random regions
   • Numbers and types of minutiae contained in random regions
     (e.g., rectangles)




                                              760122




2007 ACM Digital Rights Management Workshop     October 29, 2007   13
                                   Hash Properties
   • Secret key or password used to determine
     metric types and parameters
   • Controllable length and security (e.g., 64,
     128, or 256 bits)
   • Resistance against minor scanner
     distortions and noise



2007 ACM Digital Rights Management Workshop      October 29, 2007   14
                Fingerprint Authentication
   •      Standard authentication: Compare fingerprint scans
          against stored “correct” fingerprints.
   •      Hash-based authentication: Compare hashes of
          scanned fingerprints with stored “correct” hashes.

   •      Benefits of hashes:
         –      Actual fingerprints need not be stored for comparison.
         –      Stolen hashes do not reveal or compromise entire
                fingerprints.
         –      Key-derived hashes bind passwords and fingerprints
                tightly.
         –      Short hash length allows usage in network protocols, Web
                services, etc.


2007 ACM Digital Rights Management Workshop            October 29, 2007   15
                                          Experiments




  Original fingerprint
  Hash: 28 19 21 23 22




2007 ACM Digital Rights Management Workshop             October 29, 2007   16
                                          Experiments



                                              Distorted fingerprint
                                              Hash:        29 19 20 23 22
                                              Difference: 1 0 -1 0 0


                                                 o StirMark distortions used
                                                 o Approximation of real-life scanner distortions
  Original fingerprint
  Hash: 28 19 21 23 22




2007 ACM Digital Rights Management Workshop                                              October 29, 2007   17
                                          Experiments



                                              Distorted fingerprint         Different hash key
                                              Hash:        29 19 20 23 22   Hash :      20 26 28 21 17
                                              Difference: 1 0 -1 0 0        Difference: -8 7 7 -2 -5




  Original fingerprint
  Hash: 28 19 21 23 22




2007 ACM Digital Rights Management Workshop                                  October 29, 2007            18
                                          Experiments



                                              Distorted fingerprint         Different hash key
                                              Hash:        29 19 20 23 22   Hash :      20 26 28 21 17
                                              Difference: 1 0 -1 0 0        Difference: -8 7 7 -2 -5




  Original fingerprint
  Hash: 28 19 21 23 22




                                              Different fingerprint #1
                                              Hash:        38 17 24 34 28
                                              Difference: 10 -2 3 11 6

2007 ACM Digital Rights Management Workshop                                  October 29, 2007            19
                                          Experiments



                                              Distorted fingerprint         Different hash key
                                              Hash:        29 19 20 23 22   Hash :      20 26 28 21 17
                                              Difference: 1 0 -1 0 0        Difference: -8 7 7 -2 -5




  Original fingerprint
  Hash: 28 19 21 23 22




                                              Different fingerprint #1      Different fingerprint #2
                                              Hash:        38 17 24 34 28   Hash:        19 26 18 24 23
                                              Difference: 10 -2 3 11 6      Difference: -9 7 -3 1 1

2007 ACM Digital Rights Management Workshop                                  October 29, 2007             20
                                    Experimental Results
            90

            80

            70

            60
 Distance




            50

            40

            30

            20

            10

            0
                 0          5        10            15    20     25
                                    Fingerprint Number


                                5 random lines


                     Distances between each fingerprint and its distorted version

                     Distances between each fingerprint and other distinct fingerprints

2007 ACM Digital Rights Management Workshop                                               October 29, 2007   21
                                    Experimental Results
            90                                                                      600

            80
                                                                                    500
            70

            60                                                                      400
 Distance




                                                                         Distance
            50
                                                                                    300
            40

            30                                                                      200

            20
                                                                                    100
            10

            0                                                                        0
                 0          5        10            15    20     25                        0   5    10            15    20   25
                                    Fingerprint Number                                            Fingerprint Number


                                5 random lines                                                    50 random lines


                     Distances between each fingerprint and its distorted version

                     Distances between each fingerprint and other distinct fingerprints

2007 ACM Digital Rights Management Workshop                                                        October 29, 2007         22
                                    Experimental Results
           600                                                                     2500



           500
                                                                                   2000


           400
                                                                                   1500




                                                                        Distance
Distance




           300

                                                                                   1000
           200


                                                                                   500
           100



            0                                                                        0
                 0          5         10            15    20    25                        0   5       10            15    20   25
                                     Fingerprint Number                                              Fingerprint Number


                                50 random lines                                                     200 random lines
                                                                                                  (diminishing returns)

                     Distances between each fingerprint and its distorted version

                     Distances between each fingerprint and other distinct fingerprints

2007 ACM Digital Rights Management Workshop                                                           October 29, 2007         23
                                              Conclusion
   • Contributions
         – Methodology to extract fingerprint entropy
         – Applications in biometric authentication
                •      Address “too many passwords” problem
                •      Augment password-based schemes

   • Future work
         – Handling scanner distortions
                •      Naturally robust metrics
                •      Better error correction
                •      Explicit fingerprint synchronization
         – Applications to other biometric data
                •      Retinal blood vessels
                •      Vein patterns on hands
2007 ACM Digital Rights Management Workshop                   October 29, 2007   24

				
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